Advertisement

The Infection Algorithm: An Artificial Epidemic Approach for Dense Stereo Matching

  • Gustavo Olague
  • Francisco Fernández de Vega
  • Cynthia B. Pérez
  • Evelyne Lutton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)

Abstract

We present a new bio-inspired approach applied to a problem of stereo images matching. This approach is based on an artifical epidemic process, that we call “the infection algorithm.” The problem at hand is a basic one in computer vision for 3D scene reconstruction. It has many complex aspects and is known as an extremely difficult one. The aim is to match the contents of two images in order to obtain 3D informations which allow the generation of simulated projections from a viewpoint that is different from the ones of the initial photographs. This process is known as view synthesis. The algorithm we propose exploits the image contents in order to only produce the necessary 3D depth information, while saving computational time. It is based on a set of distributed rules, that propagate like an artificial epidemy over the images. Experiments on a pair of real images are presented, and realistic reprojected images have been generated.

Keywords

Cellular Automaton Cellular Automaton Transition Rule Stereo Image Stereo Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abbey, H.: An Examination of the Reed Frost Theory of Epidemics. Human Biology 24, 201–233 (1952)Google Scholar
  2. 2.
    Brown, M.Z., Burschka, D., Hager, G.D.: Advances in Computational Stereo. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(8), 993–1008 (2003)CrossRefGoogle Scholar
  3. 3.
    Fielding, G., Kam, M.: Weighted Matchings for Dense Stereo Correspondence. Pattern Recognition 33, 1511–1524 (2000)CrossRefGoogle Scholar
  4. 4.
    Ganesh, A.J., Kermarrec, A.-M., Massoulié, L.: Scamp: Peer-topeer lightweight membership service for large-scale group communication. In: Crowcroft, J., Hofmann, M. (eds.) NGC 2001. LNCS, vol. 2233, pp. 44–55. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  5. 5.
    Kermark, W.O., McKendrick, A.G.: A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A 115(772), 700–721 (1927)CrossRefGoogle Scholar
  6. 6.
    Louchet, J.: Using an Individual Evolution Strategy for Stereovision. Genetic Programming and Evolvable Machines Journal 2(2), 101–109 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Luo, Q., Zhou, J., Yu, S., Xiao, D.: Stereo Matching and Occlusion Detection with Integrity and Illusion Sensitivity. Pattern Recognition Letters 24, 1143–1149 (2003)zbMATHCrossRefGoogle Scholar
  8. 8.
    Maniatty, W., Szymanski, B., Caraco, T.: Parallel Computing with Generalized Cellular Automata. Nova Science Publishers, Inc., Bombay (2001)Google Scholar
  9. 9.
    Maniatty, W., Szymanski, B.K., Caraco, T.: Epidemics Modeling and Simulation on a Parallel Machine. In: IASTED, editor Proceedings of the International Conference on Applied Modeling and Simulation, Vancouver, Canada, pp. 69–70 (1993)Google Scholar
  10. 10.
    Moore, C., Newman, M.E.J.: Epidemics and Percolation in Small-World Networks. Phys. Rev. E 61, 5678–5682 (2000)CrossRefGoogle Scholar
  11. 11.
    Olague, G.: Automated Photogrammetric Network Design using Genetic Algorithms. Photogrammetric Engineering & Remote Sensing 68(5), 423–431 (2002)Google Scholar
  12. 12.
    Olague, G., Hernández, B., Dunn, E.: Accurate L-Corner Measurement using USEF Functions and Evolutionary Algorithms. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 410–421. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Olague, G., Hernández, B.: A New Accurate and Flexible Model Based Multi-corner Detector for Measurement and Recognition. Pattern Recognition Letters (to appear)Google Scholar
  14. 14.
    Keysers, D., Unger, W.: Elastic Image Matching is NP-complete. Pattern Recognition Letters 24(1-3), 445–453 (2003)zbMATHCrossRefGoogle Scholar
  15. 15.
    Sipper, M.: Evolution of Parallel Cellular Machines. Springer, Heidelberg (1997)Google Scholar
  16. 16.
    Sun, J., Zheng, N.-N., Shum, H.-Y.: Stereo Matching using Belif Propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(7), 787–800 (2003)CrossRefGoogle Scholar
  17. 17.
    Watts, D.J.: Small Worlds. Princeton University Press, Princeton (1999)Google Scholar
  18. 18.
    Lawrence Zitnick, C., Kanade, T.: A Cooperative Algorithm for Stereo Matching and Occlusion Detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(7), 675–684 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Gustavo Olague
    • 1
  • Francisco Fernández de Vega
    • 2
  • Cynthia B. Pérez
    • 1
  • Evelyne Lutton
    • 3
  1. 1.CICESE, Research Center, Applied Physics DivisionCentro de Investigación Científica y de Educación Superior de Ensenada, B.C.EnsenadaMéxico
  2. 2.Computer Science Department, Centro Universitario de MeridaUniversity of ExtremaduraMéridaSpain
  3. 3.INRIA RocquencourtComplex Team, Domaine de VoluceauLe Chesnay CedexFrance

Personalised recommendations